Hi all,
So, after a brief bout of the stupids (thanks Robert), I have
formulated my optimization problem as a physical system governed by an
ODE, and I wish to learn the equilibrium configuration of the system.
Any thoughts on what the easiest way to do this with scipy.integrate
is? Ideally, I'd just want the solver to take as large steps as
possible until things converge, and so I don't really care about the
"time" values. One option would be to use odeint and just tell it to
integrate to a distant time-point when I'm sure things will be in
equilibrium, but that seems dorky, wasteful, and potentially incorrect.
Alternately, I could use the ode class and keep asking it to integrate
small time-steps until the RMS change drops below a threshold. There,
still, I'd need to choose a reasonable time-step, and also the inner
loop would be in python instead of fortran.
Any recommendations? (Or a I again being daft? I never really took a
class in numerical methods, so sorry for dim-bulb questions!)
Zach